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This can be done by using classic algorithms whose results have been studied and compared with our proposed algorithm, genetic algorithm. Selecting an appropriate and efficient algorithm for resources scheduling is required due to dynamic feature of resources and various requests of users in cloud technology to increase efficiency. In this research, our purpose is to perform and obtain an optimal scheduling by using genetic algorithm to reach the main purpose of finding an optimum scheduling to execute tasks graph in a multi-processor structure so that total execution time or ending time of the last work unit is minimized.
International Journal of Computer Networks and Communications Security VOL 3, NO 7, JULY 2015, 271–276 Available online at: www.ijcncs.org E-ISSN 2308-9830 (Online) / ISSN 2410-0595 (Print) Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm Msc Masoumeh Alipori1, and PhD HAMID HAJ SEYYED JAVADI3 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Boroujerd, Iran Department of Computer Engineering, Boroujerd Branch, Islamic Azad University, Boroujerd, Iran Department of Applied Mathematics, Faculty of Mathematics and Computer Science, Shahed University, Tehran, Iran E-mail: 1,2alipori.m @gmail.com, 3hamid.h.s.javadi@gmail.com ABSTRACT One of the most important indexes of using cloud services in that this technology is far from the user In cloud computing systems, computing resources are presented as virtual machines In such a scenario, scheduling algorithm plays a very important role because the purpose of scheduling is tasks efficiency so that time is reduced, and resources utilization can be improved A user may use hundreds of computing resources in a cloud environment, so it is not possible to perform scheduling manually This can be done by using classic algorithms whose results have been studied and compared with our proposed algorithm, genetic algorithm Selecting an appropriate and efficient algorithm for resources scheduling is required due to dynamic feature of resources and various requests of users in cloud technology to increase efficiency In this research, our purpose is to perform and obtain an optimal scheduling by using genetic algorithm to reach the main purpose of finding an optimum scheduling to execute tasks graph in a multi-processor structure so that total execution time or ending time of the last work unit is minimized Keywords: Cloud Computing, Scheduling Algorithm, Genetic Algorithm, Classic Algorithms INTRODUCTION Evolutionary route of computing is one of the essentials in today's life In the case, the users of cloud environment try to access it on the basis of their needs Also, they consider where the service is located or how it is delivered (Pearson, 2009) Cloud computing tries to provide services in network virtual machines dynamically so that users can access to their required application programs all over the world (Charbrashkhar et al,2012) Cloud technology prevents computing resources management remotely Regardless of the used pieces and the user location, users can access to all services in cloud computing by using a browser (Pashpcosineh and Krit, 20012; Fourl et al, 2011) Selecting an appropriate and efficient algorithm is required to increase efficiency due to dynamic feature of resources and various requests of users in cloud technology (Mafiami, 2009) In cloud computing, a dynamic and new generation is provided by presenting services in dynamic and networked virtual machines, and users can access to application programs all over the world (Bouya et al, 2009) Due to dynamic feature of resources and requests in cloud computing, allocation of resources to requests is considered as NP-complete problems In recent years, one of the most important and promising methods used to solve such problems and to find optimal answer for resources scheduling is innovative methods derived from the nature These methods are similar to natural or social systems Some of these methods are genetic algorithm, chakoos optimization algorithm, and optimization of particles mass Nature-oriented algorithm is a global optimization technique based on population It is widely applied in most optimization fields due to its simple search 272 M Alipori and H H S Javadi / International Journal of Computer Networks and Communications Security, (7), July 2015 mechanism, computing efficiency and complexity and easy implementation (Houmar, 2012) Since immediate execution of processors is considered in cloud computing, a suitable scheduling algorithm can increase the speed and use maximum processing power of resources (Yousefinezhad et al, 2012) CLASSIC ALGORITHMS Scheduling must be performed in such a way that it can minimize total execution time of program on the basis of tasks time and the relationship between processors Scheduling problem of tasks graph is a NP-Hard problem (Grace and Johnson, 1979) Hence approaches based on deterministic methods are efficient Using evolutionary processing algorithm, mostly genetic algorithm, chakoos algorithm and other revolutionary algorithm are efficient to solve this problem due to their undeterministic feature In previous approaches, tasks graph in multi-processor architecture of nongenetic algorithm has been proposed to solve scheduling problem In this cases, there are six important non-genetic algorithms involving LAST (Wong et al, 1998; Bechester and Patel, 1989), EFT (Gari and Johnson, 1979), HLEFT (Sih and Li, 1993), EZ( Couria et al, 1999), MCP (Kaffman, 1976; Weo et al , 1990), and LC (Dadhi and Ahmad, 1995) Among these algorithms, MCP is the best non-genetic algorithm to solve this problem so it is used to evaluate and compare other algorithms GENETIC ALGORITHM Evolutionary account was presented in 1960 for the first time by Richembery, He studied evolution strategy Later, his theory was studied and investigated by many researchers Then, genetic algorithm presented by John Holland in 1975 in Michigan University Genetic algorithm has been inspired from genetic science and Darwin’s theory of evolution, and it is based on the survival of the best or natural selection A common application of genetic algorithm is using it as an optimization function Genetic algorithm is a useful tool in terms of pattern detection, feature selection, image perception and machinery learning This algorithm has been inspired from nature, and it is based on evolutionary principle of survival of the fittest Although genetic algorithm was proposed after evolutionary strategy algorithm, it is the most wellknown method among evolutionary algorithms In genetic algorithm, population survives according to their desirability Those having better capability will have more marriage and reproduction chance Therefore, children are created with better efficiency after some generations In genetic algorithm, each person is introduced as a chromosome The chromosomes are completed in each generation Chromosomes are evaluated in each generation, and they survive on the basis of their value Reproduction is considered in genetic algorithm by using host operators and mutation The superior parents are selected on the basis of fitness function In each execution step of genetic algorithms, some points of search space are randomly processed A sequence of characters is attributed to each point, and genetic operations are applied on these sequences Then, the obtained sequences are decoded so that new points are obtained in search space Their participants in the next step are determined by considering the value of objective function in each point Genetic algorithms are oriented and random optimization method, and they gradually move toward the optimal point It can be said that, in comparison to other optimization methods, genetic algorithm is an algorithm that can be applied for every problem without knowing the problem and without applying any limitations on its variables Also, it has proved efficiency in finding global optimum This method can solve complex optimization problems, or it can perceive reliable and valid global optimum Genetic algorithm is used to search in large space, and this map is usually used to solve special problems The results obtained from this algorithm show that it is one of the best map methods (Zhang et al, 2011) When are meta-heuristic and innovative methods used? Simple problems with too large samples (due to large dimensions of the problem, solving it precisely it costly) Simple problems with much real time limitations (dynamic optimization problems) Optimization problems with objective functions or time-consuming limitations (high computing cost in precise methods) Non-analytical models Complex simulation problems Control problems (such as traffic management in which decisions are in the range of the seconds) 273 M Alipori and H H S Javadi / International Journal of Computer Networks and Communications Security, (7), July 2015 THE WAY OF DECODING CHROMOSOME Each allocation is shown as a string constituting of a solution Each string is used as a genetic algorithm chromosome Figure 8-2 shows a view of a chromosome use hundreds of computing resources Therefore, scheduling cannot be performed manually An important point of scheduling is keeping information related to resources (Zhang Yan et al, 2011) The main assumptions are as follows: 1)Every machine can process several tasks each time (by using virtual machine) Table 1: A sample of chromosome used in genetic algorithm VM1 VM2 VM3 VMn Host1 Host2 Host3 … Hostn An appropriate resource has been considered in each solution and for each virtual machine among physical hosts It shows that each virtual machine is allocated to physical host in related column of the map Each chromosome involves N genes constituting the chromosome length The number of genes can be the number of tasks entered to the system or prepared tasks Data center of cloud system is responsible for collecting and keeping information related to operation power in resources and its other features (Lois et al, 2009) In the method, a resource is considered for each task to provide a chromosome from the ordered virtual list of available resources, and it is derived from the resource list of cloud system data center This list is updated to perform some operations like creating initial population, cross over and mutation SCHEDULING Scheduling of resources is very important in cloud computing environment Each user may face with hundreds of virtual resources in executing the task in cloud computing environment However, allocating the tasks to virtual resources by the user is impossible Scheduling system controls various tasks in the cloud system to increase the rate of task completion ability of resources and computing power Cloud computing is highly dependent on virtualization It can be said that clouds are virtual branches so tasks scheduling in various and heterogeneous physical machines is a crucial task Scheduling in cloud computing environment has been considered as one of the most important challenges in cloud computing In cloud computing systems, computing resources are presented as virtual machines In such a scenario, scheduling algorithm plays a very important role because the purpose of scheduling is the efficiency of tasks to decrease the time and to improve resources utilization In a cloud environment, the user may METHODS AND COMMENTS 2)Each task is processed just by one machine each time Hence, one operation cannot be allocated to two resources simultaneously In this regard, we define fitness function FF = min(execution times) + FL 3) Tasks are continuosly paformed, and they must not be stopped 4) Objective function of the model refers to minimizing time of executing whole project in a way that transposition relations are followed among activities The last phase occurring after scheduling is integrating empty spaces resulting from scheduling and repetition, so the response time reduces The input can be explained as directed acyclic graph called tasks graph of G=(V,E) [9] Each node is a member of V set, and it shows a task unit of the program The weight of these nodes determines execution time of related task unit Also, this graph involves a set of edges, E, showing prerequisite relations among task units When there is an edge in the form of (ti,tj) , tj can not initiate the executing until ti completes its execution These edges are weighted, and the weight of each edge shows communication cost and sending the message between two task units This cost of time is considered when two related task units are executed in different processors If the processors are same, then communication cost will be Zero THE PROPOSED ALGORITHM The proposed algorithm performs its tasks by considering middle population rather than random population In this algorithm, computing the earliest time for executing each node is required for the list of task units' priorities This parameter is shown by Ttlevel (ni), and it is the length of longest route from initiation node to ni regardless of execution time of ni node Along with executing this value, 274 M Alipori and H H S Javadi / International Journal of Computer Networks and Communications Security, (7), July 2015 critical path and its length can be determined This algorithm has been designed in such a way that it tries to make the critical path shorter and to reduce inter-processor communication cost Communication cost will be Zero among them by executing two task units on a unit processor In this section, the proposed algorithm for graph of figure 15 is studied and compared with the algorithms of GA, DCP, MD, DSC and MCP Fig Gantt graph obtained from DSC algorithm with processors Fig Gantt graph obtained from MD algorithm with processors Performance Processors Performance Processors P1 P2 P3 Average performance processors 76.9% 15.3% 13.4% 7.6% 28.3% Fig An example of task graph with 18 tasks P1 P2 P3 Average performance processors 95.45% 15.9% 25% 45.45% P4 Fig Gantt graph obtained from DCP algorithm with processorss Fig Gantt graph obtained from MCP algorithm with processorss In figure 2, Gantt chart of algorithms displayed in the table of figure is demonstrated in the task of figure along with processors efficiency in graph of figure Performance Processors P1 P2 Average processors 90.9% 45.45% 68.17% Performance Processors P1 93.4 % P2 4.3 % P3 6.5 % P4 8.6 % P5 8.6 % P6 8.6 % Average performa nce processo rs 21.66% performance Fig Gantt graph obtained from GA with processors 275 M Alipori and H H S Javadi / International Journal of Computer Networks and Communications Security, (7), July 2015 Table 2: The results obtained from MCP, DSC, MD, DCP and genetic algorithms GA 440 DCP 440 MD 3 460 DSC 460 MCP 520 The algorithm Number of processors End time Fig comparing the efficiency of processors condition We compare the proposed algorithm with wellknown algorithm of MCP and some other algorithm Also, we consider the graphs in which the result of their scheduling is specified under these algorithms As it has been shown, the result of presented algorithm is better than mentioned classic algorithms The proposed algorithm is executed quicker than other algorithms when the number of tasks increases In addition, the proposed algorithm has more stability, and in most cases, optimal 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